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tao-train-pose-classification

Pose classification using ST-GCN (Spatial Temporal Graph Convolutional Network). Classifies skeleton sequences into action categories from pose-keypoint data. Use when training, evaluating, exporting, or running inference for a TAO pose-classification model. Trigger phrases include "train pose classification", "skeleton action recognition", "ST-GCN", "keypoint sequence classifier".

skills/tao-train-pose-classification/SKILL.md

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# Pose Classification

> **Standalone install?** If this session was not initialized by the TAO skill bank plugin, run the `tao-setup` skill first (host preflight, credentials, cross-skill discovery).

Pose classification using ST-GCN (Spatial Temporal Graph Convolutional Network). Classifies skeleton sequences into action categories from pose keypoint data.

Typically trained from scratch on skeleton data.

The packaged PyTorch Pose Classification CLI supports `dataset_convert`, `train`, `evaluate`, `export`, and `inference`. `dataset_convert` is conditional: run it only when the input is raw DeepStream BodyPose JSON. If the dataset is already converted to TAO-ready `.npy` / `.pkl` files, start directly with `train` on those files and mark dataset conversion as `not run: preconverted dataset provided` in validation reports. This model does not expose deploy, prune, quantize, or standalone retrain actions. Resume/retrain behavior uses `pose_classification train -e ...` with `train.resume_training_checkpoint_path` populated.

## Quick Start (docker run)

Docker-native launch — no TAO SDK and no Python on the host. Use the local
Docker/platform skill instead when it gives a stricter environment-specific